Equivalence Testing for Factor Invariance Assessment with Categorical Indicators

  • W. Holmes FinchEmail author
  • Brian F. French
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 265)


Factorial invariance assessment is central in the development of educational and psychological instruments. Establishing factor structure invariance is key for building a strong validity argument, and establishing the fairness of score use. Fit indices and guidelines for judging a lack of invariance is an ever-developing line of research. An equivalence testing approach to invariance assessment, based on the RMSEA has been introduced. Simulation work demonstrated that this technique is effective for identifying loading and intercept noninvariance under a variety of conditions, when indicator variables are continuous and normally distributed. However, in many applications indicators are categorical (e.g., ordinal items). Equivalence testing based on the RMSEA must be adjusted to account for the presence of ordinal data to ensure accuracy of the procedures. The purpose of this simulation study is to investigate the performance of three alternatives for making such adjustments, based on work by Yuan and Bentler (Sociological Methodology, 30(1):165–200, 2000) and Maydeu-Olivares and Joe (Psychometrika 71(4):713–732, 2006). Equivalence testing procedures based on RMSEA using this adjustment is investigated, and compared with the Chi-square difference test. Manipulated factors include sample size, magnitude of noninvariance, proportion of noninvariant indicators, model parameter (loading or intercept), and number of indicators, and the outcomes of interest were Type I error and power rates. Results demonstrated that the \( T_{3} \) statistic (Asparouhov & Muthén, 2010) in conjunction with diagonally weighted least squares estimation yielded the most accurate invariance testing outcome.


Invariance testing Equivalence test Categorical indicator 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ball State UniversityMuncieUSA
  2. 2.Washington State UniversityPullmanUSA

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